A BAYESIAN NON PARAMETRIC APPROACH TO LEARN ...
Type de document :
Communication dans un congrès avec actes
Titre :
A BAYESIAN NON PARAMETRIC APPROACH TO LEARN DICTIONARIES WITH ADAPTED NUMBERS OF ATOMS
Auteur(s) :
Dang, Hong-Phuong [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chainais, Pierre [Auteur]
Centrale Lille
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Chainais, Pierre [Auteur]
Centrale Lille
Titre de la manifestation scientifique :
IEEE Workshop on Machine Learning for Signal Processing MLSP'2015
Ville :
Boston
Pays :
Etats-Unis d'Amérique
Date de début de la manifestation scientifique :
2015-09-17
Titre de l’ouvrage :
Proceedings of IEEE Workshop on Machine Learning for Signal Processing
Date de publication :
2015
Mot(s)-clé(s) en anglais :
Index Terms— sparse representations
dictionary learning
inverse problems
Indian Buffet Process
dictionary learning
inverse problems
Indian Buffet Process
Discipline(s) HAL :
Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Informatique [cs]/Apprentissage [cs.LG]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]
Résumé en anglais : [en]
Learning redundant dictionaries for sparse representation from sets of patches has proven its efficiency in solving inverse problems. In many methods, the size of the dictionary is fixed in advance. Moreover the optimization ...
Lire la suite >Learning redundant dictionaries for sparse representation from sets of patches has proven its efficiency in solving inverse problems. In many methods, the size of the dictionary is fixed in advance. Moreover the optimization process often calls for the prior knowledge of the noise level to tune parameters. We propose a Bayesian non parametric approach which is able to learn a dictionary of adapted size : the adequate number of atoms is inferred thanks to an Indian Buffet Process prior. The noise level is also accurately estimated so that nearly no parameter tuning is needed. Numerical experiments illustrate the relevance of the resulting dictionaries.Lire moins >
Lire la suite >Learning redundant dictionaries for sparse representation from sets of patches has proven its efficiency in solving inverse problems. In many methods, the size of the dictionary is fixed in advance. Moreover the optimization process often calls for the prior knowledge of the noise level to tune parameters. We propose a Bayesian non parametric approach which is able to learn a dictionary of adapted size : the adequate number of atoms is inferred thanks to an Indian Buffet Process prior. The noise level is also accurately estimated so that nearly no parameter tuning is needed. Numerical experiments illustrate the relevance of the resulting dictionaries.Lire moins >
Langue :
Anglais
Comité de lecture :
Oui
Audience :
Internationale
Vulgarisation :
Non
Commentaire :
Best Paper Award
Collections :
Source :
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